the basics of network computing
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The Basics of Network Computing. Michael T. Heaney University of Michigan August 31, 2011 3-Hour lesson. Plan for the Afternoon. Choosing a Network Program Working with Network Data Basic network statistics Visualization. Principal Tasks of Network Computing. Visualization of Networks - PowerPoint PPT PresentationTRANSCRIPT
MICHAEL T. HEANEY
UNIVERSITY OF MICHIGANAUGUST 31 , 20113-HOUR LESSON
The Basics of Network Computing
Plan for the Afternoon
Choosing a Network ProgramWorking with Network DataBasic network statisticsVisualization
Principal Tasks of Network Computing
Visualization of Networks
Calculation of Descriptive Statistics
Advanced Network Analysis (e.g., ERGM)
When considering which statistical package to use, consider which of the above tasks your work will focus on.
UCINet
Operates well in the familiar windows environment, but may be difficult to use with Apple computers.
Allows calculation of most standard network statistics, but is less adept at handling advanced analysis (e.g., ERGM).
Point-and-click approach is relatively easy to learn, but it can be a bit clunky.
Available here: http://www.analytictech.com/ucinet/download.htm
Statnet in R
Operates well in both Windows and Apple computing environments
Performs both basic and advanced network analyses
Users can develop own network analysis routines
Steep learning curve
Available here: http://statnetproject.org/
Some Other Packages
MelNet – Specializes in Exponential Random Graph Models. Available: http://www.sna.unimelb.edu.au/
Pajek – Specializes in large network analysis. Available: http://vlado.fmf.uni-lj.si/pub/networks/pajek/
SoNIA – Visualizing Dynamic Networks. Available: http://www.stanford.edu/group/sonia/
And more…..
UCINet
A good place to start training even if you are going to shift to another program.
Importing Data
Simplest approach is to read an Excel file.
1. Open UCINet2. Click on Spreadsheet Icon3. File Open Excel Files Filename.xlsx4. In this case, open Hrmatrix.xlsx5. Save as UCINET 7 dataset6. Note the creation of two files filename.##h
and filename. ##d – you will need both of these files in order to use UCINET data.
Data List Files
A good alternative when you are working with large data sets
Create using a simple text file:
dl nr = 1945 nc = 525, format = edgelist2,labels embeddeddata:10270716051 Communist10270716049 UFPJ10270716048 BrooklynPeace10270716045 BrooklynPeace10270716045 UFPJ
Read a Data List File
Data Import Text File DL… Contact_Network_Data OK
More Varied DL Formats for Data
Best to learn this on your own using UCINet help
Help Help Topics DL
Basic Data Analysis – Density
Network Cohesion (new) Density Overall Hrmatrix
Compute Density with Two-Mode Data
Network 2-Mode networks 2-mode Cohesion Input 2-mode incidence matrix OK
Basic Network Analysis – Centrality
Network Centrality and Power Multiple Measures (old)
Using Your Centrality Data in Statistical Analysis
Spreadsheet File Open CentralitySave as type ExcelExcel File Open
Compute Centrality with Two-Mode Data
Network 2-Mode Networks 2-Mode Centrality Input 2-mode matrix Contact_Network_Data.##h OK
Convert Two-Mode Data to One-Mode Data
Data Affiliations (2-mode to 1-mode) Input data … Contact_Network_Data Which mode Column [for this particular example]
Using Your Affiliation Data
Note that your new one-mode data (i.e., affiliation data) has been saved as a new file: Contact_Network_Data-ColAff
You can conduct all network analysis on this dataset
Let’s look at it:Spreadsheet File Open
Contact_Network_Data-ColAff OKNote that your cells make are counts of
affiliations, which is why we call this affiliation data
Dichotomizing Data
Are data may be valued, but we may preferred that they be dichotomous
Transform Dichotomize Contact_Network_Data-ColAff
Our output will now have only 1s and 0s
Basic Visualization
Visualize NetdrawFile Open Ucinet Dataset Network
Choose File
Refine Visualization
Open Ucinet dataset Attribute data HRattributes
Properties Lines Arrow Heads Visible Off
Properties Nodes Symbols Size Attribute Based Age
Properties Nodes Symbols Shape Attribute Based English_language
Layout Graph-Theoretic Layout Spring Embedding OK
A New View of the Network
Visualizing Contact Network Data
UCINet Spreadsheet File Open Excel Files Hybrid_Variable.xlsx
File Save As UCINet 7 dataset Hybrid_Variable
Visualize NetdrawFile Open Ucinet Dataset Network
Contact_Network_Data-ColAff File Open Ucinet Dataset Attribute data Hybrid_Variable
Visualizing Contact Network Data – Continued
Click on delete isolates buttonsLayout Graph Theoretic Layout Spring
Embedding (You may need to do this twice)Analysis Components OK
Visualizing Contact Network Data – Continued
Click on MC button to look at main component only
Turn off labels, arrow headsRepeat spring embeddingProperties Lines Size Tie Strength 1
to 10Properties Nodes Symbols Shape
Attribute Based Select Attribute Hybrid Variable OK
Click a node Choose label visible
Visualizing Contact Network Data – Continued
Analysis Subgroups Factions 2 (or 3 or 4) Go!
Next Steps
Multiplex VisualizationsThree Dimensional VisualizationsAdvanced analysis Exponential Random
Graph Models